gammagl.models.SimpleHGNModel

class SimpleHGNModel(feature_dims, hidden_dim, edge_dim, heads_list, num_etypes, num_classes, num_layers, activation, feat_drop, attn_drop, negative_slope, residual, beta)[source]

This is a model SimpleHGN from Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks paper.

Parameters:

  • feature_dims (list) – Dimension list of feature vectors in original input.

  • hidden_dim (int) – Dimension of feature vector in AGNN.

  • edge_dim – The edge dimension.

  • heads_list (list) – The list of the number of heads in each layer.

  • num_etypes (int) – The number of the edge type.

  • num_classes (int) – The number of the output classes.

  • num_layers (int) – The number of layers we used.

  • activation – Activation function we used.

  • feat_drop (float) – The feature drop rate.

  • attn_drop (float) – The attention score drop rate.

  • negative_slope (float) – The negative slope used in the LeakyReLU.

  • residual (bool) – Whether we need the residual operation.

  • beta (float) – The hyperparameter used in edge residual.

forward(x, edge_index, e_feat)[source]